\section{Related works}\label{sec:related}
Two related research fields of our work are trust in communication network and trust in Artificial Intelligent. 
\draft{compare with trust in wireless network, p2p ... utility model in them is too simple, not intrinsic or with concern of human psychology}
\draft{In web communities, such like OSNs, each actor is a Distributed Intelligent Agency. But our model needs to be more simple, no neural network...} 
\para{Models based on centrality in Graph Theory:} This group of models finds the importance of a node in a network. However, Importance doesn't necessarily mean trustworthy. In OSNs, agents are independent with each other, authorities are significantly ignored. First such method for ranking the values of a nation�s various industrial sectors in 1941, and the theory developed by Google Pagerank and HITS (Jon Kleinberg).  Centrality theory also used in trust models, advocate, appleseed, in which trust is not based on personal perceive, it is totally decided by system prediction.

\para{Models based on utility }
Buy designing payment scheme of virtual currency. Trust players will be rewarded.
\textcolor{red}{(example)} CONFIDENCE, TrustCent. Researches found, individuals are not simply driven by utility, other factors, such as risk and competent, plays important roles as well, in our trust decision. In other words, trust is based on  intrinsic utilities which combines all or those factors. 

\para{Models based on perceiving behaviors:} Learn from past to predict future. Machine learning.  This kind of model is heavily used in p2p and Wireless network. Our work is  different from them on following facts: the actor is Intelligent agent (e.g., human in OSNs) which has human-like reasoning process. 
Specifically our model has four part: short-term and long-term memory, intrinsic utility, weighted propagation, and reciprocation.